Skip to main contentWhat Makes AgentVoice Different
Unlike traditional IVR systems that force callers through rigid menu trees, AgentVoice agents understand natural language and adapt to how people actually talk. The platform uses advanced language models optimized for real-time conversation that handle interruptions, background noise, and natural speech patterns.
Core Architecture
Voice AI Models: Advanced language models optimized for real-time conversation with sub-second response times and natural turn-taking.
Knowledge System: Vector-based retrieval that gives agents access to your organization’s information from websites, documents, audio files, and images.
Scripts: Structured conversation flows that guide agents through specific interactions while maintaining natural dialogue.
Actions System: Customizable tools that allow agents to interact with external systems through webhooks and APIs.
Task Scheduling: Calendar-based system for scheduling and managing agent activities including calls, texts, emails, and API actions.
Integration Layer: Connections to your CRM and other business systems through API and webhooks.
How It Fits Your Tech Stack
AgentVoice connects to your existing systems through webhooks, custom actions, and a REST API. You can trigger calls programmatically, sync contact data with your CRM, and export conversation logs for analysis.
Voice AI Fundamentals
Voice presents unique challenges compared to text-based automation. Responses must be generated in sub-second timeframes to feel natural. Background noise, accents, and unclear speech require robust recognition. Conversations need appropriate silence, verbal acknowledgments, and turn-taking. Voice interactions feel more personal and mistakes are more noticeable.
Understanding these differences helps you design agents that excel in voice-first scenarios. 
